Drug Metabolism

Assessing Drug Metabolism Using the Simcyp Simulator

The Simcyp® Simulator extrapolates in vivo metabolic clearance from routine in vitro data generated during drug development using a variety of systems, including human liver microsomes (HLM), human intestinal microsomes (HIM), human hepatocytes (HHEP), recombinant cytochrome P450 and UGT enzymes (rCYP, rUGT), and cytosolic or S9 tissue fractions. Some of its unique features include:

The ability to predict clearance in a population with intersubject variability, including covariation with age

Experimental values of fumic for new compounds are either provided by the user or may be predicted using the Prediction Toolbox within the Simcyp Simulator from physicochemical properties (acid-base-neutral class; ionization state; log logPo:w)

Using Intersystem Extrapolation Factors (ISEFs) to convert data obtained using rCYPs to a human liver microsome environment, thereby allowing reliable extrapolation of human drug clearance from these in vitro systems

Gut metabolism is accommodated by a model that takes account of both enterocytic permeability and enterocytic blood flow as determinants of gut metabolism.

In cases where in vitro kinetic data are lacking, the parameter estimation and retrograde modules can be used to derive these data from clinical pharmacokinetic inputs using a ‘top-down’ approach.

Extrapolating drug metabolic clearance from in vitro data

The in vitro intrinsic clearance (CLint) of a compound can be determined either from the enzyme kinetic parameters Vmax and Kmor, more simply, from the rate of metabolism at a single substrate concentration, provided it is much lower than the Km value.

This in vitro data can be scaled up using the relevant scaling factors: milligrams of microsomal protein per gram of liver (MPPGL; Barter et al., 2006), hepatocellularity (HHEP; Barter et al., 2006), enzyme abundances (Rowland-Yeo et al., 2003) and total liver weight (Johnson et al., 2005). Each of these scaling factors has been the subject of extensive, in-house meta-analysis of literature data.